ForeAct: Geometry-Guided Action Learning from World Representations
Overview
This paper presents ForeAct, a method for robotic manipulation that integrates scene geometry and dynamic evolution within a single-pass representation backbone. By repurposing a pretrained video world model and using learnable tokens supervised by next-frame relative depth, ForeAct eliminates the need for iterative diffusion or rectified-flow inference and avoids auxiliary geometry modules at deployment. The approach achieves 96.6% average success on LIBERO, 63.5% on SimplerEnv-Bridge, and a 74.4% success rate across 90 real-world rollouts without additional large-scale pretraining.
Generated from attributed reports · Updated 4 hours ago
Event evidence and corrections
0 attributed source owners. Ownership does not establish independent confirmation. Quantities are reported separately and are never added together.
Reported quantity · Reported success rate: 74.4 percent · reported trials
“ency, geometry-aware control without depth input or an external geometry backbone. Without additional large-scale pretraining, ForeAct achieves 96.6% average success on LIBERO, 63.5% on SimplerEnv-Bridge, and a 74.4% success rate across 90 real-world rollouts.”
Exact source · revision 1Source owner not reported
Report timeline
Follow attributed reports and material updates.
- arXiv Robotics — research abstractsSignalForeAct: Geometry-Guided Action Learning from World Representations
This paper presents ForeAct, a method for robotic manipulation that integrates scene geometry and dynamic evolution within a single-pass representation backbone. By repurposing a pretrained video world model and using learnable tokens supervised by next-frame relative depth, ForeAct eliminates the need for iterative diffusion or rectified-flow inference and avoids auxiliary geometry modules at deployment. The approach achieves 96.6% average success on LIBERO, 63.5% on SimplerEnv-Bridge, and a 74.4% success rate across 90 real-world rollouts without additional large-scale pretraining.
Event coverage history
There is not enough continuous observation data to show a trend.
Timezone · UTC
Article dates follow your selected timezone. Briefing editions use Hong Kong time (UTC+8).